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README.md

+# Ovis2.6
+## 论文
+[Ovis2.5 Technical Report](https://arxiv.org/abs/2508.11737)
+[Ovis: Structural Embedding Alignment for Multimodal Large Language Model](https://arxiv.org/abs/2405.20797)
+
+## 模型简介
+我们推出 Ovis2.6-30B-A3B，这是 Ovis 系列多模态大语言模型（MLLM）的最新进展。在 Ovis2.5 的坚实基础上，Ovis2.6 将 LLM 主干网络升级为 混合专家（Mixture-of-Experts, MoE） 架构，在显著降低推理成本的同时实现了卓越的多模态性能。此外，该模型还在长上下文与高分辨率理解、结合主动图像分析的视觉推理，以及信息密集型文档理解方面带来了重大改进。<br>
+超越 GPT-5.2：在 HealthBench、HealthBench-Hard、幻觉评估和 SCAN-bench 等多项指标上全面超越 OpenAI 最新模型，树立医疗 AI 新的 SOTA。<br>
+
+<div align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/IPsQk8gTTMD-ipTye3WED.png" width="100%" />
+</div>
+
+## 核心特性
+MoE 架构：卓越性能，低推理成本<br>
+LLM 主干网络已升级为 混合专家（Mixture-of-Experts, MoE） 架构。这使得 Ovis2.6 能够扩展至 总计 300 亿参数，从而捕获海量知识与细微差异。关键在于，其推理过程中仅激活约 30 亿参数，确保了较低的推理成本和高吞吐量。<br>
+增强的长序列与高分辨率处理能力<br>
+Ovis2.6 将上下文窗口扩展至 64K tokens，并支持最高 2880×2880 的图像分辨率，显著提升了处理高分辨率及信息密集型视觉输入的能力。这些改进在 长文档问答 场景中尤为有效，此时模型需从多页内容中收集并整合线索以得出正确答案。<br>
+“以图思考”（Think with Image）<br>
+我们引入了 “以图思考” 能力，将视觉从被动输入转变为主动的认知工作区。在推理过程中，模型可主动调用视觉工具（如裁剪和旋转），在其思维链（Chain-of-Thought）中重新审视和分析图像区域，从而实现对视觉输入的多轮、自省式推理，提升复杂任务的准确性。<br>
+强化的 OCR、文档与图表理解能力<br>
+延续我们在信息密集型视觉任务上的专注，我们进一步增强了模型在 光学字符识别（OCR）、文档理解 和 图表/示意图分析 方面的能力。Ovis2.6 不仅能精准地从视觉数据中提取结构化信息，还能对提取内容进行 推理。<br>
+
+<div align="center">
+    <img src="https://cdn-uploads.huggingface.co/production/uploads/658a8a837959448ef5500ce5/3_A0CA-oO0Ie_WoigjAwo.png" width="100%" />
+</div>
+
+
+
+## 环境依赖
+
+|     软件     |                      版本                      |
+| :----------: | :--------------------------------------------: |
+|     DTK      |                    26.04.2                     |
+|    python    |                    3.10.12                     |
+| transformers |                     4.57.6                     |
+|     vllm     | 0.15.1+das.opt1.alpha.dtk2604.20260220.g2799735a | 
+|    torch     |  2.9.0+das.opt1.dtk2604.20260206.g275d08c2     | 
+|    numpy     |                    1.25.0                      |
+|    pillow    |                    11.3.0                      |
+|   moviepy    |                    1.0.3                       |
+|  accelerate  |                    1.12.0                      |
+|  flash_attn  | 2.6.1+das.opt1.dtk2604.torch291.20260210.g7808665e |
+
+推荐使用镜像：harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220
+
+- 挂载地址`-v`根据实际模型情况修改
+
+```bash
+docker run -it \
+    --shm-size 200g \
+    --network=host \
+    --name ovis2.6 \
+    --privileged \
+    --device=/dev/kfd \
+    --device=/dev/dri \
+    --device=/dev/mkfd \
+    --group-add video \
+    --cap-add=SYS_PTRACE \
+    --security-opt seccomp=unconfined \
+    -u root \
+    -v /opt/hyhal/:/opt/hyhal/:ro \
+    -v /path/your_code_data/:/path/your_code_data/ \
+    harbor.sourcefind.cn:5443/dcu/admin/base/custom:vllm0.15.1-ubuntu22.04-dtk26.04-0130-py3.10-20260220 bash
+```
+更多镜像可前往[光源](https://sourcefind.cn/#/service-list)下载使用。
+
+关于本项目DCU显卡所需的特殊深度学习库可从[光合](https://developer.sourcefind.cn/tool/)开发者社区下载安装，moviepy库需要单独安装。
+
+```bash
+pip install moviepy==1.0.3
+```
+
+## 数据集
+
+`暂无`
+
+## 训练
+
+`暂无`
+
+## 推理
+
+### transformers
+#### 单机推理
+```bash
+python inference.py
+```
+##### 多图输入
+```bash
+python inference-mul.py
+```
+##### 视频输入
+```bash
+python inference-mov.py
+```
+
+
+## 效果展示
+<div align=center>
+    <img src="./doc/result.png"/>
+</div>
+
+
+
+
+
+### 精度
+`DCU与GPU精度一致，推理框架：transformers`
+
+## 预训练权重
+| 模型名称  | 权重大小  | DCU型号  | 最低卡数需求 |下载地址|
+|:-----:|:----------:|:----------:|:---------------------:|:----------:|
+| Ovis2.6 | 30B | BW1000  | 2  | [Modelscope](https://www.modelscope.cn/models/AIDC-AI/Ovis2.6-30B-A3B) |
+
+## 源码仓库及问题反馈
+- https://developer.sourcefind.cn/codes/modelzoo/baichuan-m3_pytorch
+
+

inference-mov.py

+import torch
+import requests
+from PIL import Image
+from modelscope import AutoModelForCausalLM
+#from moviepy.editor import VideoFileClip  # pip install moviepy==1.0.3
+
+from moviepy import VideoFileClip
+
+# Thinking mode & budget
+enable_thinking = True
+enable_thinking_budget = True  # Only effective if enable_thinking is True.
+
+# Total tokens for thinking + answer. Ensure: max_new_tokens > thinking_budget + 25
+max_new_tokens = 2048
+thinking_budget = 1024
+
+model = AutoModelForCausalLM.from_pretrained(
+    "AIDC-AI/Ovis2.6-30B-A3B",
+    dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+
+# Video inference
+
+video_file = "./doc/mm.mp4"
+num_frames = 8
+
+with VideoFileClip(video_file) as clip:
+    total_frames = int(clip.fps * clip.duration)
+    indices = [int(i * total_frames / num_frames) for i in range(num_frames)]
+    frames = [Image.fromarray(clip.get_frame(t)) for t in (idx / clip.fps for idx in indices)]
+
+messages = [{"role": "user", "content": [
+    {"type": "video", "video": frames},
+    {"type": "text", "text": "Describe this video in detail."},
+]}]
+
+input_ids, pixel_values, grid_thws = model.preprocess_inputs(messages=messages, add_generation_prompt=True, max_pixels=896*896)
+input_ids = input_ids.cuda()
+pixel_values = pixel_values.cuda().to(model.dtype) if pixel_values is not None else None
+grid_thws = grid_thws.cuda() if grid_thws is not None else None
+
+with torch.no_grad():
+    outputs = model.generate(inputs=input_ids, pixel_values=pixel_values, grid_thws=grid_thws,
+                             max_new_tokens=1024, do_sample=True,
+                             eos_token_id=model.text_tokenizer.eos_token_id,
+                             pad_token_id=model.text_tokenizer.pad_token_id)
+print(model.text_tokenizer.decode(outputs[0], skip_special_tokens=True))

inference-mul.py

+import torch
+import requests
+from PIL import Image
+from modelscope import AutoModelForCausalLM
+
+# Thinking mode & budget
+enable_thinking = True
+enable_thinking_budget = True  # Only effective if enable_thinking is True.
+
+# Total tokens for thinking + answer. Ensure: max_new_tokens > thinking_budget + 25
+max_new_tokens = 2048
+thinking_budget = 1024
+
+model = AutoModelForCausalLM.from_pretrained(
+    "AIDC-AI/Ovis2.6-30B-A3B",
+    dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+
+
+# Multi-image inference
+multi_image_files = [
+    "./doc/cs.png",
+    "./doc/dog.png",
+    "./doc/p1.png",
+]
+
+content = [{"type": "image", "image": Image.open(p).convert("RGB")} for p in multi_image_files]
+content.append({"type": "text", "text": "Describe the images."})
+messages = [{"role": "user", "content": content}]
+
+input_ids, pixel_values, grid_thws = model.preprocess_inputs(messages=messages, add_generation_prompt=True, max_pixels=896*896)
+input_ids = input_ids.cuda()
+pixel_values = pixel_values.cuda().to(model.dtype) if pixel_values is not None else None
+grid_thws = grid_thws.cuda() if grid_thws is not None else None
+
+with torch.no_grad():
+    outputs = model.generate(inputs=input_ids, pixel_values=pixel_values, grid_thws=grid_thws,
+                             max_new_tokens=1024, do_sample=True,
+                             eos_token_id=model.text_tokenizer.eos_token_id,
+                             pad_token_id=model.text_tokenizer.pad_token_id)
+print(model.text_tokenizer.decode(outputs[0], skip_special_tokens=True))

inference.py

+import torch
+import requests
+from PIL import Image
+from modelscope import AutoModelForCausalLM
+
+# Thinking mode & budget
+enable_thinking = True
+enable_thinking_budget = True  # Only effective if enable_thinking is True.
+
+# Total tokens for thinking + answer. Ensure: max_new_tokens > thinking_budget + 25
+max_new_tokens = 2048
+thinking_budget = 1024
+
+model = AutoModelForCausalLM.from_pretrained(
+    "AIDC-AI/Ovis2.6-30B-A3B",
+    dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto"
+)
+
+local_image_path= "./doc/page.png"
+messages = [{
+    "role": "user",
+    "content": [
+        {"type": "image", "image": Image.open(local_image_path)},
+        {"type": "text", "text": "Calculate the sum of the numbers in the middle box in figure (c)."},
+    ],
+}]
+
+input_ids, pixel_values, grid_thws = model.preprocess_inputs(
+    messages=messages,
+    add_generation_prompt=True,
+    enable_thinking=enable_thinking
+)
+input_ids = input_ids.cuda()
+pixel_values = pixel_values.cuda() if pixel_values is not None else None
+grid_thws = grid_thws.cuda() if grid_thws is not None else None
+
+outputs = model.generate(
+    inputs=input_ids,
+    pixel_values=pixel_values,
+    grid_thws=grid_thws,
+    enable_thinking=enable_thinking,
+    enable_thinking_budget=enable_thinking_budget,
+    max_new_tokens=max_new_tokens,
+    thinking_budget=thinking_budget,
+)
+
+response = model.text_tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)

model.properties

+# 模型唯一标识
+modelCode=2152
+# 模型名称
+modelName=Baichuan-M3_pytorch
+# 模型描述
+modelDescription=Baichuan-M3 是百川智能推出的全新一代医疗增强大语言模型，是继 Baichuan-M2 之后的重要里程碑。
+# 运行过程
+processType=推理
+# 算法类别
+appCategory=对话问答
+# 框架类型
+frameType=transformers,vllm
+# 加速卡类型
+accelerateType=BW1000
\ No newline at end of file